到目前為止，我們一直專注于定義由序列輸入、單個(gè)隱藏 RNN 層和輸出層組成的網(wǎng)絡(luò)。盡管在任何時(shí)間步長的輸入和相應(yīng)的輸出之間只有一個(gè)隱藏層，但從某種意義上說這些網(wǎng)絡(luò)很深。第一個(gè)時(shí)間步的輸入會(huì)影響最后一個(gè)時(shí)間步的輸出T（通常是 100 或 1000 步之后）。這些輸入通過T在達(dá)到最終輸出之前循環(huán)層的應(yīng)用。但是，我們通常還希望保留表達(dá)給定時(shí)間步長的輸入與同一時(shí)間步長的輸出之間復(fù)雜關(guān)系的能力。因此，我們經(jīng)常構(gòu)建不僅在時(shí)間方向上而且在輸入到輸出方向上都很深的 RNN。這正是我們在 MLP 和深度 CNN 的開發(fā)中已經(jīng)遇到的深度概念。

構(gòu)建這種深度 RNN 的標(biāo)準(zhǔn)方法非常簡單：我們將 RNN 堆疊在一起。給定一個(gè)長度序列T，第一個(gè) RNN 產(chǎn)生一個(gè)輸出序列，也是長度T. 這些依次構(gòu)成下一個(gè) RNN 層的輸入。在這個(gè)簡短的部分中，我們將說明這種設(shè)計(jì)模式，并提供一個(gè)簡單示例來說明如何編寫此類堆疊 RNN。下面，在 圖 10.3.1中，我們用L隱藏層。每個(gè)隱藏狀態(tài)對順序輸入進(jìn)行操作并產(chǎn)生順序輸出。此外，每個(gè)時(shí)間步的任何 RNN 單元（圖 10.3.1中的白框 ）都取決于同一層在前一時(shí)間步的值和前一層在同一時(shí)間步的值。

圖 10.3.1深度 RNN 的架構(gòu)。

正式地，假設(shè)我們有一個(gè)小批量輸入 Xt∈Rn×d（示例數(shù)量： n，每個(gè)示例中的輸入數(shù)量：d) 在時(shí)間步 t. 同時(shí)step，讓hidden state的 lth隱藏層（l=1,…,L） 是 Ht(l)∈Rn×h（隱藏單元的數(shù)量：h) 和輸出層變量是 Ot∈Rn×q（輸出數(shù)量： q). 環(huán)境Ht(0)=Xt, 的隱藏狀態(tài)lth使用激活函數(shù)的隱藏層?l計(jì)算如下：

(10.3.1)Ht(l)=?l(Ht(l?1)Wxh(l)+Ht?1(l)Whh(l)+bh(l)),

權(quán)重在哪里 Wxh(l)∈Rh×h和 Whh(l)∈Rh×h, 連同偏差bh(l)∈R1×h, 是模型參數(shù)lth隱藏層。

最終輸出層的計(jì)算只是根據(jù)最終的隱藏狀態(tài)Lth隱藏層：

(10.3.2)Ot=Ht(L)Whq+bq,

重量在哪里Whq∈Rh×q和偏見bq∈R1×q是輸出層的模型參數(shù)。

與 MLP 一樣，隱藏層的數(shù)量L和隱藏單元的數(shù)量h是我們可以調(diào)整的超參數(shù)。常見的 RNN 層寬度 (h) 在范圍內(nèi)(64,2056), 和共同深度 (L) 在范圍內(nèi)(1,8). 此外，我們可以通過將(10.3.1)中的隱藏狀態(tài)計(jì)算替換為來自 LSTM 或 GRU 的計(jì)算來輕松獲得深度門控 RNN。

import torch
from torch import nn
from d2l import torch as d2l

from mxnet import np, npx
from mxnet.gluon import rnn
from d2l import mxnet as d2l

npx.set_np()

import jax
from flax import linen as nn
from jax import numpy as jnp
from d2l import jax as d2l

import tensorflow as tf
from d2l import tensorflow as d2l

10.3.1。從零開始實(shí)施

要從頭開始實(shí)現(xiàn)多層 RNN，我們可以將每一層視為RNNScratch具有自己可學(xué)習(xí)參數(shù)的實(shí)例。

class StackedRNNScratch(d2l.Module):
  def __init__(self, num_inputs, num_hiddens, num_layers, sigma=0.01):
    super().__init__()
    self.save_hyperparameters()
    self.rnns = nn.Sequential(*[d2l.RNNScratch(
      num_inputs if i==0 else num_hiddens, num_hiddens, sigma)
                  for i in range(num_layers)])

class StackedRNNScratch(d2l.Module):
  def __init__(self, num_inputs, num_hiddens, num_layers, sigma=0.01):
    super().__init__()
    self.save_hyperparameters()
    self.rnns = [d2l.RNNScratch(num_inputs if i==0 else num_hiddens,
                  num_hiddens, sigma)
           for i in range(num_layers)]

class StackedRNNScratch(d2l.Module):
  num_inputs: int
  num_hiddens: int
  num_layers: int
  sigma: float = 0.01

  def setup(self):
    self.rnns = [d2l.RNNScratch(self.num_inputs if i==0 else self.num_hiddens,
                  self.num_hiddens, self.sigma)
           for i in range(self.num_layers)]

class StackedRNNScratch(d2l.Module):
  def __init__(self, num_inputs, num_hiddens, num_layers, sigma=0.01):
    super().__init__()
    self.save_hyperparameters()
    self.rnns = [d2l.RNNScratch(num_inputs if i==0 else num_hiddens,
                  num_hiddens, sigma)
           for i in range(num_layers)]

多層正向計(jì)算只是逐層進(jìn)行正向計(jì)算。

@d2l.add_to_class(StackedRNNScratch)
def forward(self, inputs, Hs=None):
  outputs = inputs
  if Hs is None: Hs = [None] * self.num_layers
  for i in range(self.num_layers):
    outputs, Hs[i] = self.rnns[i](outputs, Hs[i])
    outputs = torch.stack(outputs, 0)
  return outputs, Hs

@d2l.add_to_class(StackedRNNScratch)
def forward(self, inputs, Hs=None):
  outputs = inputs
  if Hs is None: Hs = [None] * self.num_layers
  for i in range(self.num_layers):
    outputs, Hs[i] = self.rnns[i](outputs, Hs[i])
    outputs = np.stack(outputs, 0)
  return outputs, Hs

@d2l.add_to_class(StackedRNNScratch)
def forward(self, inputs, Hs=None):
  outputs = inputs
  if Hs is None: Hs = [None] * self.num_layers
  for i in range(self.num_layers):
    outputs, Hs[i] = self.rnns[i](outputs, Hs[i])
    outputs = jnp.stack(outputs, 0)
  return outputs, Hs

@d2l.add_to_class(StackedRNNScratch)
def forward(self, inputs, Hs=None):
  outputs = inputs
  if Hs is None: Hs = [None] * self.num_layers
  for i in range(self.num_layers):
    outputs, Hs[i] = self.rnns[i](outputs, Hs[i])
    outputs = tf.stack(outputs, 0)
  return outputs, Hs

例如，我們在時(shí)間機(jī)器數(shù)據(jù)集上訓(xùn)練了一個(gè)深度 GRU 模型（與第 9.5 節(jié)相同）。為了簡單起見，我們將層數(shù)設(shè)置為 2。

data = d2l.TimeMachine(batch_size=1024, num_steps=32)
rnn_block = StackedRNNScratch(num_inputs=len(data.vocab),
               num_hiddens=32, num_layers=2)
model = d2l.RNNLMScratch(rnn_block, vocab_size=len(data.vocab), lr=2)
trainer = d2l.Trainer(max_epochs=100, gradient_clip_val=1, num_gpus=1)
trainer.fit(model, data)

data = d2l.TimeMachine(batch_size=1024, num_steps=32)
rnn_block = StackedRNNScratch(num_inputs=len(data.vocab),
               num_hiddens=32, num_layers=2)
model = d2l.RNNLMScratch(rnn_block, vocab_size=len(data.vocab), lr=2)
trainer = d2l.Trainer(max_epochs=100, gradient_clip_val=1, num_gpus=1)
trainer.fit(model, data)

data = d2l.TimeMachine(batch_size=1024, num_steps=32)
rnn_block = StackedRNNScratch(num_inputs=len(data.vocab),
               num_hiddens=32, num_layers=2)
model = d2l.RNNLMScratch(rnn_block, vocab_size=len(data.vocab), lr=2)
trainer = d2l.Trainer(max_epochs=100, gradient_clip_val=1, num_gpus=1)
trainer.fit(model, data)

data = d2l.TimeMachine(batch_size=1024, num_steps=32)
with d2l.try_gpu():
  rnn_block = StackedRNNScratch(num_inputs=len(data.vocab),
               num_hiddens=32, num_layers=2)
  model = d2l.RNNLMScratch(rnn_block, vocab_size=len(data.vocab), lr=2)
trainer = d2l.Trainer(max_epochs=100, gradient_clip_val=1)
trainer.fit(model, data)

10.3.2。簡潔的實(shí)現(xiàn)

幸運(yùn)的是，實(shí)現(xiàn)多層 RNN 所需的許多邏輯細(xì)節(jié)都可以在高級 API 中輕松獲得。我們的簡潔實(shí)現(xiàn)將使用此類內(nèi)置功能。該代碼概括了我們之前在第 10.2 節(jié)中使用的代碼，允許明確指定層數(shù)而不是選擇單層的默認(rèn)值。

class GRU(d2l.RNN): #@save
  """The multi-layer GRU model."""
  def __init__(self, num_inputs, num_hiddens, num_layers, dropout=0):
    d2l.Module.__init__(self)
    self.save_hyperparameters()
    self.rnn = nn.GRU(num_inputs, num_hiddens, num_layers,
             dropout=dropout)

Fortunately many of the logistical details required to implement multiple layers of an RNN are readily available in high-level APIs. Our concise implementation will use such built-in functionalities. The code generalizes the one we used previously in Section 10.2, allowing specification of the number of layers explicitly rather than picking the default of a single layer.

class GRU(d2l.RNN): #@save
  """The multi-layer GRU model."""
  def __init__(self, num_hiddens, num_layers, dropout=0):
    d2l.Module.__init__(self)
    self.save_hyperparameters()
    self.rnn = rnn.GRU(num_hiddens, num_layers, dropout=dropout)

Flax takes a minimalistic approach while implementing RNNs. Defining the number of layers in an RNN or combining it with dropout is not available out of the box. Our concise implementation will use all built-in functionalities and add num_layers and dropout features on top. The code generalizes the one we used previously in Section 10.2, allowing specification of the number of layers explicitly rather than picking the default of a single layer.

class GRU(d2l.RNN): #@save
  """The multi-layer GRU model."""
  num_hiddens: int
  num_layers: int
  dropout: float = 0

  @nn.compact
  def __call__(self, X, state=None, training=False):
    outputs = X
    new_state = []
    if state is None:
      batch_size = X.shape[1]
      state = [nn.GRUCell.initialize_carry(jax.random.PRNGKey(0),
          (batch_size,), self.num_hiddens)] * self.num_layers

    GRU = nn.scan(nn.GRUCell, variable_broadcast="params",
           in_axes=0, out_axes=0, split_rngs={"params": False})

    # Introduce a dropout layer after every GRU layer except last
    for i in range(self.num_layers - 1):
      layer_i_state, X = GRU()(state[i], outputs)
      new_state.append(layer_i_state)
      X = nn.Dropout(self.dropout, deterministic=not training)(X)

    # Final GRU layer without dropout
    out_state, X = GRU()(state[-1], X)
    new_state.append(out_state)
    return X, jnp.array(new_state)

Fortunately many of the logistical details required to implement multiple layers of an RNN are readily available in high-level APIs. Our concise implementation will use such built-in functionalities. The code generalizes the one we used previously in Section 10.2, allowing specification of the number of layers explicitly rather than picking the default of a single layer.

class GRU(d2l.RNN): #@save
  """The multi-layer GRU model."""
  def __init__(self, num_hiddens, num_layers, dropout=0):
    d2l.Module.__init__(self)
    self.save_hyperparameters()
    gru_cells = [tf.keras.layers.GRUCell(num_hiddens, dropout=dropout)
           for _ in range(num_layers)]
    self.rnn = tf.keras.layers.RNN(gru_cells, return_sequences=True,
                    return_state=True, time_major=True)

  def forward(self, X, state=None):
    outputs, *state = self.rnn(X, state)
    return outputs, state

選擇超參數(shù)等架構(gòu)決策與10.2 節(jié)中的決策非常相似。我們選擇相同數(shù)量的輸入和輸出，因?yàn)槲覀冇胁煌臉?biāo)記，即vocab_size。隱藏單元的數(shù)量仍然是 32。唯一的區(qū)別是我們現(xiàn)在通過指定 的值來選擇不平凡的隱藏層數(shù)量 num_layers。

gru = GRU(num_inputs=len(data.vocab), num_hiddens=32, num_layers=2)
model = d2l.RNNLM(gru, vocab_size=len(data.vocab), lr=2)
trainer.fit(model, data)

model.predict('it has', 20, data.vocab, d2l.try_gpu())

'it has a small the time tr'

gru = GRU(num_hiddens=32, num_layers=2)
model = d2l.RNNLM(gru, vocab_size=len(data.vocab), lr=2)

# Running takes > 1h (pending fix from MXNet)
# trainer.fit(model, data)
# model.predict('it has', 20, data.vocab, d2l.try_gpu())

gru = GRU(num_hiddens=32, num_layers=2)
model = d2l.RNNLM(gru, vocab_size=len(data.vocab), lr=2)
trainer.fit(model, data)

model.predict('it has', 20, data.vocab, trainer.state.params)

'it has wo mean the time tr'

gru = GRU(num_hiddens=32, num_layers=2)
with d2l.try_gpu():
  model = d2l.RNNLM(gru, vocab_size=len(data.vocab), lr=2)
trainer.fit(model, data)

model.predict('it has', 20, data.vocab)

'it has and the time travel'

10.3.3。概括

在深度 RNN 中，隱藏狀態(tài)信息被傳遞到當(dāng)前層的下一個(gè)時(shí)間步和下一層的當(dāng)前時(shí)間步。存在許多不同風(fēng)格的深度 RNN，例如 LSTM、GRU 或普通 RNN。方便的是，這些模型都可以作為深度學(xué)習(xí)框架的高級 API 的一部分使用。模型的初始化需要小心。總的來說，深度 RNN 需要大量的工作（例如學(xué)習(xí)率和裁剪）來確保適當(dāng)?shù)氖諗俊?/p>

10.3.4。練習(xí)

用 LSTM 替換 GRU 并比較準(zhǔn)確性和訓(xùn)練速度。

增加訓(xùn)練數(shù)據(jù)以包含多本書。你的困惑度可以降到多低？

在建模文本時(shí)，您想結(jié)合不同作者的來源嗎？為什么這是個(gè)好主意？會(huì)出什么問題？